{"title":"利用人工神经网络预测并网太阳能光伏发电装置的日前输出","authors":"R. Ehsan, S. P. Simon, P. R. Venkateswaran","doi":"10.1109/ICEMELEC.2014.7151201","DOIUrl":null,"url":null,"abstract":"Solar Photovoltaic (PV) systems are gaining popularity as a form of alternative energy with increased environmental awareness, renewable energy usage and concern for energy security. Lack of area-specific forecasts for the power output of grid-connected photovoltaic system hinders tapping solar power on a large scale. The objective of this paper is to estimate the profile of produced power of a grid-connected 20 kWp solar power plant in a reputed manufacturing industry located in Tiruchirappalli, India [10° 44' 42.3816\" N, 78° 47' 9.4524\" E]. An Artificial Neural Network (ANN)-based model is proposed in this paper. An experimental database of solar power output (from 7th January 2014 to 10th February 2014) has been used for training the ANN. Simulations were carried out with the Neural Network Fitting Toolbox of MATLAB software. Day-Ahead Forecasting results indicate that the proposed model performs well with great accuracy and efficiency. Statistical error analysis in terms of Mean Absolute Percentage Error (MAPE) was conducted and the best result was found to be 0.2887%. Reliable area-specific solar power production map can provide better utilization of solar energy resource and help in power system management.","PeriodicalId":186054,"journal":{"name":"2014 IEEE 2nd International Conference on Emerging Electronics (ICEE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Day-ahead prediction of solar power output for grid-connected solar photovoltaic installations using Artificial Neural Networks\",\"authors\":\"R. Ehsan, S. P. Simon, P. R. Venkateswaran\",\"doi\":\"10.1109/ICEMELEC.2014.7151201\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Solar Photovoltaic (PV) systems are gaining popularity as a form of alternative energy with increased environmental awareness, renewable energy usage and concern for energy security. Lack of area-specific forecasts for the power output of grid-connected photovoltaic system hinders tapping solar power on a large scale. The objective of this paper is to estimate the profile of produced power of a grid-connected 20 kWp solar power plant in a reputed manufacturing industry located in Tiruchirappalli, India [10° 44' 42.3816\\\" N, 78° 47' 9.4524\\\" E]. An Artificial Neural Network (ANN)-based model is proposed in this paper. An experimental database of solar power output (from 7th January 2014 to 10th February 2014) has been used for training the ANN. Simulations were carried out with the Neural Network Fitting Toolbox of MATLAB software. Day-Ahead Forecasting results indicate that the proposed model performs well with great accuracy and efficiency. Statistical error analysis in terms of Mean Absolute Percentage Error (MAPE) was conducted and the best result was found to be 0.2887%. Reliable area-specific solar power production map can provide better utilization of solar energy resource and help in power system management.\",\"PeriodicalId\":186054,\"journal\":{\"name\":\"2014 IEEE 2nd International Conference on Emerging Electronics (ICEE)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE 2nd International Conference on Emerging Electronics (ICEE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEMELEC.2014.7151201\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 2nd International Conference on Emerging Electronics (ICEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEMELEC.2014.7151201","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
摘要
随着环保意识的增强、可再生能源的使用和对能源安全的关注,太阳能光伏(PV)系统作为一种替代能源形式越来越受欢迎。缺乏对并网光伏发电系统输出功率的区域预测,阻碍了太阳能发电的大规模开发。本文的目的是估计位于印度Tiruchirappalli(10°44' 42.3816" N, 78°47' 9.4524" E)的著名制造业中并网的20 kWp太阳能发电厂的发电量概况。本文提出了一种基于人工神经网络(ANN)的模型。使用太阳能输出实验数据库(2014年1月7日至2014年2月10日)训练人工神经网络。利用MATLAB软件中的神经网络拟合工具箱进行仿真。日前预测结果表明,该模型具有较高的精度和效率。采用平均绝对百分比误差(MAPE)进行统计误差分析,最佳结果为0.2887%。可靠的区域太阳能发电量分布图可以更好地利用太阳能资源,帮助电力系统管理。
Day-ahead prediction of solar power output for grid-connected solar photovoltaic installations using Artificial Neural Networks
Solar Photovoltaic (PV) systems are gaining popularity as a form of alternative energy with increased environmental awareness, renewable energy usage and concern for energy security. Lack of area-specific forecasts for the power output of grid-connected photovoltaic system hinders tapping solar power on a large scale. The objective of this paper is to estimate the profile of produced power of a grid-connected 20 kWp solar power plant in a reputed manufacturing industry located in Tiruchirappalli, India [10° 44' 42.3816" N, 78° 47' 9.4524" E]. An Artificial Neural Network (ANN)-based model is proposed in this paper. An experimental database of solar power output (from 7th January 2014 to 10th February 2014) has been used for training the ANN. Simulations were carried out with the Neural Network Fitting Toolbox of MATLAB software. Day-Ahead Forecasting results indicate that the proposed model performs well with great accuracy and efficiency. Statistical error analysis in terms of Mean Absolute Percentage Error (MAPE) was conducted and the best result was found to be 0.2887%. Reliable area-specific solar power production map can provide better utilization of solar energy resource and help in power system management.